from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
def mnist_demo():
    #加载数据集
    mnist = input_data.read_data_sets('/Users/taotao/Desktop/mnist手写数字识别',one_hot=True)
    images,labels = mnist.train.next_batch(100)
    # print('images.shape:',images.shape,'labels.shape:',labels.shape)
    #1。准备数据
    x = tf.placeholder(dtype=tf.float32,shape=[None,784])
    y_true = tf.placeholder(dtype=tf.float32,shape=[None,10])
    #2。构建模型 x(None,784)*weight(784,10)+bias=y(None,10)
    weight = tf.Variable(initial_value=tf.random_normal(shape=[784,10]))
    bias = tf.Variable(initial_value=tf.random_normal(shape=[10]))
    y_predict = tf.matmul(x,weight)+bias
    #3。构建损失函数 softmax 交叉熵损失
    error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_predict))

    #4。优化损失
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(error)
    #初始化变量
    init = tf.global_variables_initializer()
    #开启会话
    with tf.Session() as sess:
        #运行初始化变量
        sess.run(init)
        print('训练模型前的损失：%f'%(sess.run(error,feed_dict={x:images,y_true:labels})))
        #训练
        for i in range(100):
            op,loss=sess.run([optimizer,error],feed_dict={x:images,y_true:labels})
            print('第%d次训练后的损失：%f'%(i+1,loss))

if __name__ == '__main__':
    mnist_demo()